Markov-type state models to describe non-Markovian dynamics

TL;DR

This paper evaluates advanced methods for constructing Markov state models that accurately capture non-Markovian dynamics in molecular systems, improving the estimation of transition timescales and population decay.

Contribution

It compares several novel approaches to estimate transition matrices in non-Markovian molecular dynamics, including Laplace-transform, microstate-to-macrostate projection, quasi-MSM, and hybrid methods.

Findings

Laplace-transform method improves timescale estimation.

Microstate-to-macrostate projection yields correct population dynamics.

Hybrid approach combines short-time MD with long-time MSM.

Abstract

When clustering molecular dynamics (MD) trajectories into a few metastable conformational states, the Markov state models (MSMs) assumption of timescale separation between fast intrastate fluctuations and rarely occurring interstate transitions is often not valid. Hence, the naive estimation of the macrostate transition matrix via simply counting transitions between the states leads to significantly too short implied timescales and thus to too fast population decays. In this work, we discuss advanced approaches to estimate the transition matrix. Assuming that Markovianity is at least given at the microstate level, we consider the Laplace-transform based method by Hummer and Szabo, as well as a direct microstate-to-macrostate projection, which by design yields correct macrostate population dynamics. Alternatively, we study the recently proposed quasi-MSM ansatz of Huang and coworkers to solve a generalized master equations, as well as a hybrid method that employs MD at short times and MSM at long times. Adopting a one-dimensional toy model and an all-atom folding trajectory of HP35, we discuss the virtues and shortcomings of the various approaches.